Carbohydrates are central to the study of cancer. Glycosylation sites are used as biomarkers for cancer detection, and cell surface glycans play a role in metastasis, signaling, and transcription processes in tumor cells. Despite the importance of carbohydrates in cancer, structural knowledge of glycosylation is currently limited. Carbohydrates have proven difficult both to characterize chemically and to model computationally. They have a high degree of conformational freedom, multiple stereocenters, and in many cases complicated branching. Traditional modeling methods have focused on proteins and nucleic acids, which have distinct properties from carbohydrates, and much progress remains in modeling the interactions between proteins and carbohydrates, particularly in the case of glycoproteins. The major objectives of this proposed research are to develop tools to predict the interactions between carbohydrates and protein structures. Newly developed methods will handle the high degree of flexibility, stereochemistry, and branching inherent to carbohydrates. Available scoring functions for protein folding and docking will be improved, custom tailoring them to function as readily with glycopeptides as with non-glycosylated peptides. These improved and novel methods will be used to model the MUC1 protein, which plays several roles in cancer, including computational binding studies of adhesion molecules with MUC1 glycoforms involved in cancer and of proteolytic enzymes to MUC1. Collaborations with the Yarema lab will ensure the validity of the results. Success in these endeavors will lead to greater understanding of the role of glycosylation in human biology and could result ultimately in anti-cancer therapeutics and vaccines.